Member of Technical Staff, Fireworks AI

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field, completed within the last 6 months or before the end of 2026
• Strong programming fundamentals and solid grounding in data structures, algorithms, and systems
• Practical software engineering experience through internships, research, open-source contributions, or substantial personal projects
• Proficiency in at least one of Python, C++, Go, Rust, TypeScript, or a comparable language
• Fluency with AI coding harnesses and a habit of using them well
• Clear written and verbal communication, and the judgment to ask for help early
• Comfort with ambiguity, fast iteration, and high ownership

Desirable

• This role is built for new graduates who want to work on real AI systems, in production, from the start
• As a MTS at Fireworks, you'll write and ship code that runs on one of the busiest inference platforms in the world — serving hundreds of models to developers and enterprises at massive scale
• We hire new grads into teams across Engineering, and we match you to a team based on your strengths and interests after we get to know you
• Depending on where you land, your work could span inference and performance, model training and fine-tuning, distributed systems and cloud infrastructure, developer platform and APIs, or product and full-stack application engineering
• One application puts you in front of all of them
• You'll join with a senior engineer as your mentor and a structured ramp-up: you'll start on well-scoped, real problems alongside your mentor, then take ownership of features and systems of your own
• We move fast, our bar is high, and we'll invest in getting you there
• We welcome candidates who have completed a Bachelor's or Master's degree within the last 6 months, or expect to complete one by Summer 2027
• Note that candidates graduating in December 2026 are highly preferred and prioritized
• Ship production code: Design, build, and deploy features and services that customers depend on every day
• Own problems end to end: Take a problem from ambiguous idea through design, implementation, testing, rollout, and operation
• Make things faster and more reliable: Improve the performance, efficiency, and scalability of the systems you work on
• Work with modern AI systems: Build with, on top of, or underneath large models — depending on your team, that could mean serving them, training them, evaluating them, or building products powered by them
• Debug hard things: Dig into unfamiliar code and unfamiliar failures, and get to the root cause
• Collaborate broadly: Work closely with engineers, researchers, product, and — on some teams — directly with customers